2024-03-28T16:45:33Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1351012024-03-13T10:09:45Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134488
Corchado Rodríguez, Emilio Santiago
Corchado Rodríguez, Juan Manuel
Aiken, Jim
Lefevre, Nathalie
Smyth, Tim
2017-09-06T09:16:33Z
2017-09-06T09:16:33Z
2004/08
Advances in Case-Based Reasoning Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 3155, pp. 533-546.
978-3-540-22882-0 (Print)/ 978-3-540-28631-8 (Online)
0302-9743 (Print) / 1611-3349 (Online)
http://hdl.handle.net/10366/135101
By improving accuracy in the quantification of the ocean’s CO2 budget, a more precise estimation can be made of the terrestrial fraction of global CO2 budget and its subsequent effect on climate change. First steps have been taken towards this from an environmental and economic point of view, by using an instance based reasoning system, which incorporates a novel clustering and retrieval method – a Cooperative Maximum Likelihood Hebbian Learning model (CoHeL). This paper reviews the problems of measuring the ocean’s CO2 budget and presents the CoHeL model developed and outlines the IBR system developed to resolve the problem.
application/pdf
en
Springer Science + Business Media
Attribution-NonCommercial-NoDerivs 3.0 Unported
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Computer Science
Quantifying the Ocean’s CO2 Budget with a CoHeL-IBR System
info:eu-repo/semantics/article